American Journal of Chemical Engineering 2019; 7(2): 71-80

http://www.sciencepublishinggroup.com/j/ajche

doi: 10.11648/j.ajche.20190702.13

ISSN: 2330-8605 (Print); ISSN: 2330-8613 (Online)

Effect of Oleylamine Concentration and Operating Conditions on Ternary Nanocatalyst for Fischer-Tropsch Synthesis Using Response Surface Methodology

Tahereh Taherzadeh Lari1, *

, Hamid Reza Bozorgzadeh2, Hossein Atashi

3,

Abdol Mahmood Davarpanah4, Ali Akbar Mirzaei

1

1Department of Chemistry, Faculty of Science, University of Sistan and Baluchestan, Zahedan, Iran 2Catalyst Division, Research Institute of Petroleum Industry (RIPI), Tehran, Iran 3Department of Chemical Engineering, Faculty of Engineering, University of Sistan and Baluchestan, Zahedan, Iran 4Department of Physics, Faculty of Science, University of Sistan and Baluchestan, Zahedan, Iran

Email address:

*Corresponding author

To cite this article: Tahereh Taherzadeh Lari, Hamid Reza Bozorgzadeh, Hossein Atashi, Abdol Mahmood Davarpanah, Ali Akbar Mirzaei. Effect of Oleylamine

Concentration and Operating Conditions on Ternary Nanocatalyst for Fischer-Tropsch Synthesis Using Response Surface Methodology.

American Journal of Chemical Engineering. Vol. 7, No. 2, 2019, pp. 71-80. doi: 10.11648/j.ajche.20190702.13

Received: May 29, 2019; Accepted: July 1, 2019; Published: July 12, 2019

Abstract: The Fe-Co-Ce nanocatalyst was synthesized by a solvothermal method and used in Fischer-Tropsch synthesis.

This paper represents a statistical analysis to illustrate the effects of oleylamine concentration and operating variables

(temperature, pressure, inlet H2/CO molar ratio) on light olefin (C2=-C4

=), paraffin (C1 + C2-C4) selectivity and CO conversion

(catalyst activity) in a fixed bed micro reactor was done. In order to evaluate variable effects, analysis of variance (ANOVA)

was applied for modeling and optimization of goal products using response surface methodology (RSM). The result showed

that by increasing both amine concentration and pressure at lower temperature and inlet H2/CO molar ratio, olefin selectivity

and CO conversion rises, while paraffin selectivity reduces. Comparison of optimization results to maximum olefin selectivity

and CO conversion and minimum paraffin selectivity for predicted and experimental data indicate a desirable agreement.

Keywords: Fischer-Tropsch Synthesis, Response Surface Methodology, Optimization, Fe-Co-Ce Nanocatalyst,

Oleylamine Concentration, Operating Conditions

1. Introduction

In the near future the feedstock of chemical industry will

shift from crude oil to natural gas because of the limited

reserves of crude oil and increasing environmental constraints.

Fischer-Tropsch synthesis is a promising process, is known as

an exothermic polymerization reaction, which converts CO

and H2 into water and linear hydrocarbons (chemical

liquefaction of natural gas) [1, 2]. The main active industrially

metals for Fischer-Tropsch catalysts are based on iron and

cobalt. The price for iron-based catalysts is low, but these

catalysts suffer from a low wax selectivity, deactivation and

inhibition of productivity by water at high syngas conversions.

However, cobalt-based catalysts are stable, promoting

formation of heavy wax and permit high syngas conversions

[3, 4]. Rare earth oxides have been extensively illustrated as

both structural and electronic promoters to boost catalyst

features. Among rare earth elements, CeO2 is the most

prominent metal oxide in industrial catalysis process. The

activity of CeO2 as a promoter has some controversy

viewpoint [5]. Solid catalysts are highly complicated products

derived from chemicals by various procedures.

The catalytic features of heterogeneous catalysts are

greatly affected by both every step of fabrication (such as

temperature, time, pH, pressure and concentration) and

operating conditions [6]. Solvothermal synthesis is comprises

of heating of solvents and metal compounds, which

coordinated in attendance of an organic capping agent at high

temperatures. This method included commonly three steps: (i)

72 Tahereh Taherzadeh Lari et al.: Effect of Oleylamine Concentration and Operating Conditions on Ternary

Nanocatalyst for Fischer-Tropsch Synthesis Using Response Surface Methodology

dissociation of metal precursors occurs by heating of

solution; (ii) for further particle nucleation and growth, the

solution aged at desired temperature and (iii) separation of

particles from solvent and unreacted material [7].

Compared to other synthetic methods, it has the additional

advantages of simplicity, high reaction speed, and

significant superiority of synthesis under moderate reaction

conditions. Moreover, it allows for different solvents and

surface agents to be appropriately selected. Application of a

surfactant like Oleylamine, which is a primary amine with a

long chain, has the most efficient ability to act as a solvent,

surfactant, or reducing agent [8]. As well as doing as an

electron donor at enhanced temperatures, is sufficient

enough to both limiting nanoparticle growths and avoiding

accumulation due to forming a significant superficial layer

that works as a barrier to mass transfer [9]. Most of studies

done according to traditionally experiment, which one

variable changed, whereas the others kept fix. Thus it

probably cause to incorrect results because of the

interaction effects are ignored. Response surface

methodology (RSM) is a helpful statistical method to

establish functional relationship between several

independent variables to response, diagnostic the suitability

of the model, evaluate interaction effects and optimizing

variable parameters in chemical processes [10].

The significant benefit of using statistical model for product

selectivity comparing with the previous studies, which

investigates effect of variables without present a model, is

depict both the importance of each parameters and also

illustrate interaction effects on each other. In this research, Fe–

Co–Ce (ternary) nanocatalyst synthesized by solvothermal

procedure and catalytic performance towards olefin, paraffin

selectivity and CO conversion were studied. To the best of our

knowledge, no similar research has been reported previously

on consideration of the relationship between parameters of

solvothermal synthesis and operating variables in Fischer-

Tropsch synthesis. There are some researches, which studied

solvothermal method in order to synthesis catalyst in FTS, but

the effect of synthesis parameters did not evaluated [11, 12].

The objective of the present study is illustrate the effect of

oleylamine concentrations and operating variables including

temperature, pressure, and inlet H2/CO molar ratio on catalytic

behavior. Investigation of binary, quadratic interactions,

analysis of variance, modeling, and optimization were

evaluated by RSM.

2. Experimental

2.1. Materials

All the chemicals were of analytical grade used without

further purification. Iron nitrate (II) nona hydrate

(Fe(NO3)3.9H2O, 99%), cobalt nitrate (II) hegza hydrate

(Co(NO3)2.6H2O, 99%), cerium nitrate (III) hegza hydrate

(Ce(NO3)3.6H2O, 99%), toluene (C7H8, 99%), and ethanol

(C2H5OH, 99%) were purchased from Merck. Oleylamine

(C18H37N, 70%) was purchased from Aldrich.

2.2. Nanocatalyst Synthesis

Iron-cobalt-cerium three metals were synthesized using the

solvothermal method. The preparation method can be

described briefly at different concentration of oleylamine

performs as follow: 0.5 g (1.15 mmol) of cerium nitrate, 0.32

g (0.91 mmol) of cobalt nitrate, 0.38 g (0.94 mmol) of iron

nitrate were add into 50 mL of toluene containing 5, 8 and 10

g (20.2, 29.9 and 37.4 mmol) of oleylamine. The mixture was

magnetically stirred vigorously for 1 h at room temperature

(Figure 1). The resulting mixture solution was subsequently

transferred into an 80 mL Teflon-lined autoclave and heated

to 180°C. The autoclave was sealed and maintained at the

given temperature for 18 h before it was allowed to cool

down to room temperature. The nanoparticles formed were

precipitated in the excess ethanol and further isolated from

each other by centrifugation. The resulting nanoparticles

were finally transferred to an oven to be dried before

calcination at 100 and 500°C in air for 4 h.

Figure 1. Schematic diagram of the procedure for solvothermally

synthesized nanocatalyst.

2.3. Research Catalytic Setup for Fischer-Tropsch

Synthesis Experiments

Fischer-Tropsch synthesis was performed in a stainless

fixed-bed micro reactor with an inner diameter of 12 mm.

The catalyst (1.0 g) was well dispersed with asbestos and

loaded in the center of reactor with thermocouple inside.

Three mass flow controllers (Model 5850E, Brooks

Instrument, Hatfield, PA, USA) were used to automatically

adjust the flow rate of the inlet gases containing CO, H2, and

N2 (with 99.99% purity). A mixture of CO and H2 (H2/CO

=1, flow rate of each gas 30 mL min-1

) was subsequently

introduced into the reactor, which was placed inside a tubular

furnace (Figure 2 and 3, Model ATU 150-15, Atbin). The

reaction temperature was controlled by a digital program

controller (DPC) and visually monitored by a computer

through a thermocouple inserted into the catalytic bed. The

catalyst is situ was pre-reduced under 2-bar pressure and H2

flow (with flow rate of 30 mL min-1

) at 400°C for 48 h before

the reaction started. In each test, 1.0 g of catalyst was loaded

and all data was collected after the time of 4 h to ensure

steady state operation was attained.

American Journal of Chemical Engineering 2019; 7(2): 71-80 73

Figure 2. Experimental setup of fixed bed reactor (FBR) for Fischer-Tropsch synthesis over iron-cobalt-cerium mixed oxide nanocatalyst: (1) gas cylinders,

(2) valve, (3) pressure gauge, (4) mass flow controller (MFC), (5) mixing chamber, (6) thermocouple, (7) tubular furnace, (8) fix bed reactor and catalyst bed

(reaction zone), (9) temperature digital program controller (DPC), (10) resistance temperature detector, (11) condenser, (12) trap, (13) back pressure

regulator (BPR), (14) flow meter, (15) control panel, (16) electrical motor, (17) air pump, (18) hydrogen generator, (19) gas chromatograph, (20) silica-gel

column.

Figure 3. Schematic design of fixed-bed-reactor (FBR).

74 Tahereh Taherzadeh Lari et al.: Effect of Oleylamine Concentration and Operating Conditions on Ternary

Nanocatalyst for Fischer-Tropsch Synthesis Using Response Surface Methodology

2.4. Catalytic Performance Measurement

The Fischer-Tropsch synthesis was performed with

mixture of CO and H2, in the temperature range of 270-

400°C, with inlet H2/CO molar ratio of 1-4, the space

velocity of 3600h-1

and at 2-5bar range of pressure. In each

experiment, for reactor catalyst testing at each oleylamine

concentration to avoid of deactivation effect, fresh catalyst

was loaded. An automatic backpressure regulator in order to

adjust and modify the pressure range via the TESCOM

software was used. Reactant and product streams were

analyzed by online gas chromatography (Thermo ONIX

UNICAM PROGC+) equipped with two thermal

conductivity detectors (TCD) and one flame ionization

detector (FID) with ability to analysis of a broad variety of

gaseous hydrocarbon mixtures. One TCD used for the

analysis of hydrogen (H2) and the other one used for all the

permanent gases like N2, O2 and CO. The analysis of

hydrocarbons was done by FID. The analysis of non-

condensable gases, methane through C8 hydrocarbons is

applied. The contents of the sample loop were injected

automatically into an alumina capillary column. As well as

helium (He) was employed as a carrier gas for optimum

sensitivity. The calibration was performed by various

calibration mixtures (CH4, C2H4, C2H6, C3H6, C3H8, n-C4H10,

i-C4H10, n-C5H12) and pure compounds obtained from Tarkib

Gas Alvand Company of Iran. The operation condition and

obtained data of each experiment are presented in below

Tables. The CO conversion percent calculated according to

the normalization method:

CO conversion (%) = ������ �� �� – ������ �� ���

������ �� �� × 100 (1)

2.5. Statistical Analysis

Response surface methodology (RSM) is a set of statistical

and mathematical technique to utilize, improve and

optimizing a model by affecting on multiple factors using

design of experiments (DoE) method and statistical analysis

[13]. RSM reduced and simplified experimental designs to

obtain an entire understanding of the model. As well as

achieved the optimal combination of independent variables

instead of looking for the optimal solution among a variety

number of randomly created parameters. The advantage of

RSM method for optimization than without statistic approach

is to design, formulation of products and the most important

one is predicting interaction effects of involved factors. The

RSM technique can suggest a model according to

experimental and predicted data and acquires the most

desirable model for response by adjusting the factors.

In this study, RSM method was done using historical data,

which achieved by experiment previously. The 35

experiments tested in a fixed-bed micro reactor and used for

evaluation, modeling and optimization of independent

variables of solvothermaly synthesized nanocatalyst via

Fischer-Tropsch synthesis by RSM. The empirical model

related to a response and to find out intercept, linear,

quadratic and interaction terms was used as follow:

X = t� + ∑ t�Y����� + ∑ t��Y�

����� + ∑ t��Y�

��� Y� ± ℰ (2)

Where, X is the predicted response, Y� and Y� are

independent variables, t� is the intercept coefficient, t�

represents the linear effect of Y�, t�� is the quadratic effect of

Y�, and t�� is the interaction terms of Y� and Y�, as well as, n

observes the number of experiments. The regression terms of

a response in order to statistical significant were checked by

ANOVA (analysis of variance).

3. Results and Discussion

3.1. Fischer-Tropsch Synthesis Performance

The activity and selectivity of synthesized nanocatalysts

towards products in Fischer-Tropsch synthesis are affected

by many operating (e.g. temperature, pressure, inlet H2/CO,

space velocity and etc) and fabricating conditions. In this

study, the effects of oleylamine concentration (N),

Temperature (T), pressure (P) and inlet H2/CO molar ratio

(M) on the selectivity of Olefin (C2=-C4

=), Paraffins (C1 + C2

-

-C4-), and catalyst activity (CO conversion) at operating

condition of (H2/CO=1-4, GHSV=3600 h-1

, P=2-5 bar,

T=270-400°C) investigated as a model by RSM.

3.2. Design of Experiments

RSM design for historical data is used to illustrate both the

effect of independent variables and their interactions. The

studied variables includes four parameters; A, B, C, D, for

amine concentration (cc), temperature (°C), pressure (bar),

and inlet (H2/CO) molar ratio named N, T, P, and M

respectively. According to achieved 35 experimental data, the

studied responses were olefin selectivity (C2”-C4”), paraffin

selectivity (C1 + C2-C4) and CO conversion as a catalyst

activity, which indicated in Table 1. Response surface

methodology, analysis of variance (ANOVA), modeling and

optimization of responses carried out using Design Expert

7.00 Software.

Table 1. Experimental data and catalytic performance of Fe-Co-Ce nanocatalyst synthesized by solvothermal method during FTS.

Run A B C D "#

$ "%& "'

(

N (cc) T (°C) P (bar) M= H2/CO X Olefin (%) X Paraffin (%) X CO (%)

1 5 270 2 1 19.63 52.9 52 2 5 300 2 1 26.72 55.94 29.5

3 5 330 2 1 30.35 59.04 36

4 5 350 2 1 29.08 59.61 41.8 5 5 380 2 1 23.19 61.34 45

6 5 400 2 1 16.11 62.35 49.5

American Journal of Chemical Engineering 2019; 7(2): 71-80 75

Run A B C D "#

$ "%& "'

(

N (cc) T (°C) P (bar) M= H2/CO X Olefin (%) X Paraffin (%) X CO (%)

7 8 300 2 1 16.28 53.69 34

8 8 330 2 1 21.99 60.55 37.6 9 8 350 2 1 21.93 63.34 41

10 8 380 2 1 18.69 69.23 53

11 8 400 2 1 12.14 74.17 61 12 10 300 2 1 22.62 48.82 26.6

13 10 330 2 1 28.85 54.29 31.1

14 10 350 2 1 26.87 60.36 35.5 15 10 380 2 1 23.88 67.36 47.7

16 10 400 2 1 17.89 70.5 54

17 5 300 2 1 25.38 54.4 37.5 18 5 300 4 1 32.09 50.7 43

19 5 300 5 1 27.97 61.6 44.8 20 8 300 3 1 21.99 53.68 36.7

21 8 300 4 1 22.43 56.31 52.3

22 8 300 5 1 18.01 64.31 55.9 23 10 300 2 1 22.71 46 29.1

24 10 300 3 1 28.85 43 34

25 10 300 4 1 27.9 49.44 37.5 26 10 300 5 1 22.76 61.9 58.4

27 5 300 2 2 29.8 54.2 32.6

28 5 300 2 3 24.34 58.5 38.4 29 5 300 2 4 18.53 58.03 44.5

30 8 300 2 2 21.68 66 27.9

31 8 300 2 3 19.22 66.64 41.3 32 8 300 2 4 14.49 70.29 48.2

33 10 300 2 2 26.35 57.83 29.5

34 10 300 2 3 25.61 64.6 42.8 35 10 300 2 4 22.74 68.35 54.6

a: olefin selectivity

b: paraffin selectivity

c: co conversion (catalyst activity)

3.2.1. Statistical Models of Olefin, Paraffin Selectivity and Catalyst Activity

Analysis of variance was done for all responses. According to the sequential model sum of square test, the linear model

suggested as the most suitable model in the case of X1, X2 and X3 selectivity with p-value of <0.0001. The significance of

regression coefficient verified by P-values, which is not adequate and should be lower than 0.05. Final encoded models for

independent variables in terms of named independent variables are presented in following equations:

X1 �% = −238.07270 – 21.57902 × N + 1.85368 × T + 17.25144 × P + 4.73863 × M + 0.012937 × N ×

T + 0.54577 × N × M + 1.08507 × N2 – 2.8874 × 10 − 3 × T2 − 2.44388 × P2 – 2.05799 × M2 (3)

X2 �% = + 62.64891 + 3.65542 × N – 0.041767 × T – 23.29887 × P – 4.10818 × M + 0.025667 × N × T +

1.11276 × N × M – 0.89904 × N2 + 3.76699 × P2 (4)

X3 �% = + 406.51871 – 7.83548 × N – 1.99545 × T – 14.00758 × P − 20.30212 × M + 0.032850 × N × T + 1.28708 ×

N × P + 1.35024 × N × M – 0.53842 × N2 + 2.80231 × 10 − 3 × T2 + 1.48580 × P2 + 3.03492 × M2 (5)

The X3 (CO conversion) was selected as a measure of catalyst

activity, while X1 and X2 were chosen as a measure of catalyst

selectivity. The analysis of variance (ANOVA) for X1 and X2

selectivity and catalyst activity of X3 are reported in Table 2. The

large F-value includes 88.32, 54.62 and 14.68 for X1, X2 and X3

respectively imply that the obtained models are significant. Also,

the p-value less than 5% verifies the adequacy of models.

Concerning about selectivity model of X1 (olefin); A (amine

concentration), B (Temperature), C (Pressure), D (inlet H2/CO

molar ratio) and interactions of AB, AD and A2, B

2, C

2, D

2 were

significant terms of models. Regarding about X2 (paraffin)

response; A, B, C, D and interactions of AB, AD, A2 and C

2

were significant terms of obtained model. Similarly, in the case

of X3 (CO conversion) response, A, B, C, D and interactions of

AB, AC, AD, A2, B

2, C

2 and D

2 were significant terms. In order

to improve the models, insignificant terms with p-value higher

than 0.05 were dropped.

Table 2. Analysis of variance reported for evaluation of responses.

Model F-value P-value df R2 Adj-R2 Pred-R2 Adeq Precision Lack of fit

X1 88.32 <0.0001 10 0.9735 0.9625 0.9439 39.461 0.3823

X2 54.62 <0.0001 8 0.9438 0.9266 0.8911 27.972 0.4600

X3 14.68 <0.0001 11 0.8753 0.8157 0.6375 13.418 0.6424

76 Tahereh Taherzadeh Lari et al.: Effect of Oleylamine Concentration and Operating Conditions on Ternary

Nanocatalyst for Fischer-Tropsch Synthesis Using Response Surface Methodology

Since “Lack of fit” is an undesirable characteristic of a

model, the non-significant value (greater than 0.1) of lack of

fit is good. All responses have insignificant p-value to their

lack of fit as indicated in Table 2. The differences between

“Pred R-Squared” and “Adj R-Squared” have to be (less than

0.2) so all responses are in reasonable agreement, which

shows that the obtained models predict the responses

precisely. The actual values versus predicted values are

plotted in Figure 4. All responses indicate a good correlation

between actual and predicted values. “Adeq Precision”

measures signal to noise ratio, so a ratio greater than 4 is

desirable which indicates an adequate signal. As reported in

Table 2 Adeq Precision of 39.461, 27.972 and 13.418 for X1,

X2 selectivity and X3 activity are high enough and indicate an

adequate design, so these models can be used to navigate the

design space.

The accuracy of achieved models determines by both R-

Square and evaluation of correlation coefficients between R-

Square and R-Square Adjust, which the nearer to the 1 the

better. Desirable value of R-Square achieves by adding or

reducing terms of models. R-Square Adjust is attributed to an

unbiased assessment.

Figure 4. Predicted and actual values of data plotted for (a) Olefin, (b) Paraffin and (c) CO conversion.

3.2.2. Effect of Parameters Via Model Graph

Comparing the effect of all independent variables at a

selected point in the design space is possible by perturbation

plot. Although the perturbation plot indicates sensitive

variables to response, it does not show interaction effects

among them. The sharp slope or curvature of a variable imply

that response is very sensitive to it, while relatively straight

lines show that response is insensitive to it. According to

Figure 5, the sensitivity of olefin selectivity (a) decreases like

B>C>D>A, so olefin is most sensitive to pressure variable

(B). The sensitivity of paraffin selectivity (b) reduces based

on A>B>D>C therefore paraffin have the most sensitivity to

amine concentration (A). As well as the sensitivity of catalyst

activity to CO conversion (c) decreases according to

A>C>B>D, so the result indicate that CO conversion has

higher dependence on amine concentration (A).

Figure 5. Perturbation plot for (a) X1=Olefin, (b) X2=Paraffin and (c) X3=CO conversion responses (A: amine concentration, B: temperature, C: pressure, D:

inlet H2/CO molar ratio).

In order to illustrate relationship between independent

variables and effect of them on response, three dimensional

(3D surface) and contour (2D) plots are used. The effect of

two significant terms according to perturbation plot for all

American Journal of Chemical Engineering 2019; 7(2): 71-80 77

responses interpreted, which other variables kept fix. From

Figures 6 and 7 determine that maximum value of response

(a) achieves at average values 335°C of temperature and 3.5

bar of pressure. Thus, there is a maximum for olefin

selectivity (X1) at 3D surface plot. It is obvious that by

increasing in both amine concentration and temperature, the

selectivity to paraffin (b) increases. As well as it is indicated

that the activity of synthesized nanocatalyst increases at

higher pressure and amine concentration due to increase in

CO conversion.

Figure 6. Contour plots, which illustrate the interaction effects between independent variables on responses, (a) Pressure and temperature, (b) Amine

concentration and temperature, (c) Amine concentration and pressure.

Figure 7. 3D surface plots, which illustrate interaction between independent variables on responses, (a) Pressure and temperature, (b) Amine concentration

and temperature, (c) Amine concentration and pressure.

3.2.3. Effect of Amine Concentration, Inlet H2/CO Molar

Ratio and Pressure on Catalytic Performance

The results indicate that by increasing in oleylamine

concentration, both olefin selectivity and CO conversion

increase, while paraffin selectivity decreases. There is

relevance between oleylamine concentration and particle

size, which is proven by XRD. As well as the smaller particle

size, the stronger interactions between nanocatalyst, so it is

caused higher catalyst activity. On the other hand, by

decreasing in particle size, reduction of nanocatalyst became

harder, which showed the TPR profiles shifted into higher

temperatures. Also this result is proved by VSM that

increasing in oleylamine concentration and smaller particle

size caused lower residual magnetization (Mr) and higher

coercivity (Hc), which indicates powerful interaction

between nanocatalysts that remain after leave external

magnetization field out. The desirable response (maximum

olefin selectivity and maximum CO conversion) achieved at

lower inlet H2/CO molar ratio, which was expected. The

lower H2/CO molar ratio, the higher olefin selectivity

obtained. As the result showed higher catalyst activity

achieved as a CO conversion at higher pressure. This is

because of by increasing in pressure, the concentration of

inlet gases increases, higher reaction rate and causes increase

in CO conversion.

3.2.4. Validation of Models

In order to diagnostic the adequacy of obtained models,

four plot have to be investigated, including; i) Normal

Probability plot, ii) Internally Studentized Residuals versus

predicted values plot, iii) Externally Studentized Residuals

plot, and iv) Box-Cox plot. Figure 8 indicates that all

residuals follow through a normal distribution.

78 Tahereh Taherzadeh Lari et al.: Effect of Oleylamine Concentration and Operating Conditions on Ternary

Nanocatalyst for Fischer-Tropsch Synthesis Using Response Surface Methodology

Figure 8. Normal probability plot of residuals for (a) X1=olefin, (b) X2=paraffin, (c) X3=CO conversion.

Internally studentized plot shows residual values versus predicted values of responses. This plot investigates the hypothesis

of constant variance by checking values to have constant error (between �3). As Figure 9 indicates all plots have accidentally

distribution, which represent residuals stay constant at all over the plots.

Figure 9. Internally studentized residuals plot of residuals versus predicted values, (a) X1=olefin, (b) X2=paraffin, (c) X3=CO conversion.

Externally studentized residuals plot demonstrates that the deviation value of standard deviation of actual value from

predicted value after cross a point out. Figure 10 indicates that values are between (�3.5 , so the residuals are not outliers.

Figure 10. Externally studentized residuals plot for (a) X1=olefin, (b) X2=paraffin, (c) X3=CO conversion.

Box-Cox plot is used to assist diagnosis the most suitable power transformation function in order to affect on response. The

American Journal of Chemical Engineering 2019; 7(2): 71-80 79

lowest point in Box-Cox plot shows (Figure 11) the best value of Landa, which minimum square residuals at transformed

model created. When max/min ratio of response value be higher than >3, there is more possibility to improve power function.

Also confidence range at this value is shown.

Figure 11. Box-Cox plot for power transforms of (a) X1=olefin, (b) X2=paraffin, (c) X3=CO conversion.

3.2.5. Optimization

The obtained models used to investigate optimization of all

responses. The studied goal was to maximize X1 (olefin

selectivity), X3 (CO conversion, catalyst activity) and

minimize X2 (paraffin selectivity), while all independent

variables including A (amine concentration), B (temperature),

C (pressure) and D (inlet H2/CO molar ratio) is chosen in

range. Several solutions suggested by the software. The most

desirable model in order to maximizing X1, X3 and

minimizing X2 achieved at; 10 cc for amine concentration,

322.15°C for temperature, 3.93 bar for pressure and 1 for

inlet H2/CO molar ratio. As well as olefin, paraffin selectivity

and CO conversion for catalyst activity suggested 32.0139,

52.1847 and 42.889 respectively at desirability of 0.752,

which is shown in Figure 12. The predictability of the

optimized model illustrated using experimental run. The

result obtained 33.05 for olefin, 53.29 for paraffin and 42.56

for CO conversion, which indicates desirable confidence

between predicted value and experimental.

Many researches had studied on selectivity of Fischer-

Tropsch synthesis, e.g Atashi et al [14] investigates modeling

and optimizing of products through iron catalyst. Sun et al

[15] illustrates optimization using response surface

methodology using SiO2 bimetallic Co-Ni catalyst.

Figure 12. Optimum condition for maximum value of olefin and CO conversion and minimum value of paraffin achieved at (N=10 cc, T=322°C, P=3.93 bar

and M=1).

4. Conclusion

Ternary Fe-Co-Ce nanocatalyst solvothermally synthesized

and the effect of parameters on performance of nanocatalyst

evaluated in detail. Amine concentration and pressure were

both two significant factors in activity and selectivity of

synthesized nanocatalyst in the Fischer-Tropsch synthesis.

The statistical analysis of RSM applied to investigate

individual, binary, interaction effects, modeling and

optimizing of variables. The Fischer-Tropsch synthesis

optimized at synthesis and operating parameters including

amine concentration, temperature, pressure and feed H2/CO

molar ratio. All responses optimized according to a simple

linear model. There was a confidence agreement between

predicted values and experimental. The results indicate that

by increasing both amine concentration, pressure, and

decreasing in temperature and inlet H2/CO molar ratio olefin

80 Tahereh Taherzadeh Lari et al.: Effect of Oleylamine Concentration and Operating Conditions on Ternary

Nanocatalyst for Fischer-Tropsch Synthesis Using Response Surface Methodology

selectivity and catalyst activity as CO conversion increases,

while paraffin selectivity decreases. VSM analysis indicated

that enhancing in oleylamine concentration resulted in

ferromagnetic behavior of nanocatalysts, which alters from a

soft to hard one. Magnetic measurements depicted that higher

oleylamine concentration and smaller particle size leads to

higher values of coercivity, while saturation magnetization

and residual magnetization are independent of particle size. It

was concluded from TPR profiles that oleylamine

concentration by influencing on particle size results in

increase at reduction temperature.

Conflict of Interest

All the authors do not have any possible conflicts of

interest.

Acknowledgements

The authors would like to thank and appreciate the

Ministry of Science & Research, Research Department of

Sistan & Baluchestan University, the Iranian National

Petrochemical Company (INPC) as well as Research Institute

of Petroleum Industry (RIPI) for financial supports.

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